Importing librarys

In [2]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns

Reading Data Set

In [3]:
data = pd.read_csv('2019_nCoV_data.csv')
data.head()
Out[3]:
Sno Date Province/State Country Last Update Confirmed Deaths Recovered
0 1 01/22/2020 12:00:00 Anhui China 01/22/2020 12:00:00 1.0 0.0 0.0
1 2 01/22/2020 12:00:00 Beijing China 01/22/2020 12:00:00 14.0 0.0 0.0
2 3 01/22/2020 12:00:00 Chongqing China 01/22/2020 12:00:00 6.0 0.0 0.0
3 4 01/22/2020 12:00:00 Fujian China 01/22/2020 12:00:00 1.0 0.0 0.0
4 5 01/22/2020 12:00:00 Gansu China 01/22/2020 12:00:00 0.0 0.0 0.0

Checking Dataset

In [168]:
data.dtypes
Out[168]:
Sno                        int64
Date              datetime64[ns]
Province/State            object
Country                   object
Last Update       datetime64[ns]
Confirmed                float64
Deaths                   float64
Recovered                float64
dtype: object
In [4]:
data.describe()
Out[4]:
Sno Confirmed Deaths Recovered
count 1719.000000 1719.000000 1719.000000 1719.000000
mean 860.000000 454.596859 10.441536 44.361838
std 496.376873 3712.529032 106.638286 351.078713
min 1.000000 0.000000 0.000000 0.000000
25% 430.500000 2.000000 0.000000 0.000000
50% 860.000000 12.000000 0.000000 0.000000
75% 1289.500000 109.000000 0.000000 7.000000
max 1719.000000 59989.000000 1789.000000 7862.000000
In [5]:
data.describe(include="O")
Out[5]:
Date Province/State Country Last Update
count 1719 1257 1719 1719
unique 27 61 34 520
top 02/17/2020 22:00:00 Shandong Mainland China 01/31/2020 19:00:00
freq 75 27 801 63
In [6]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1719 entries, 0 to 1718
Data columns (total 8 columns):
Sno               1719 non-null int64
Date              1719 non-null object
Province/State    1257 non-null object
Country           1719 non-null object
Last Update       1719 non-null object
Confirmed         1719 non-null float64
Deaths            1719 non-null float64
Recovered         1719 non-null float64
dtypes: float64(3), int64(1), object(4)
memory usage: 107.5+ KB
In [7]:
data.isna().sum()
Out[7]:
Sno                 0
Date                0
Province/State    462
Country             0
Last Update         0
Confirmed           0
Deaths              0
Recovered           0
dtype: int64
In [15]:
data["Country"].unique()
Out[15]:
array(['China', 'US', 'Japan', 'Thailand', 'South Korea',
       'Mainland China', 'Hong Kong', 'Macau', 'Taiwan', 'Singapore',
       'Philippines', 'Malaysia', 'Vietnam', 'Australia', 'Mexico',
       'Brazil', 'France', 'Nepal', 'Canada', 'Cambodia', 'Sri Lanka',
       'Ivory Coast', 'Germany', 'Finland', 'United Arab Emirates',
       'India', 'Italy', 'Sweden', 'Russia', 'Spain', 'UK', 'Belgium',
       'Others', 'Egypt'], dtype=object)

Exploratory data analysis

Excluding country name Others

In [19]:
data=data[data.Country!='Others']
data.head()
Out[19]:
Sno Date Province/State Country Last Update Confirmed Deaths Recovered
0 1 2020-01-22 12:00:00 Anhui China 2020-01-22 12:00:00 1.0 0.0 0.0
1 2 2020-01-22 12:00:00 Beijing China 2020-01-22 12:00:00 14.0 0.0 0.0
2 3 2020-01-22 12:00:00 Chongqing China 2020-01-22 12:00:00 6.0 0.0 0.0
3 4 2020-01-22 12:00:00 Fujian China 2020-01-22 12:00:00 1.0 0.0 0.0
4 5 2020-01-22 12:00:00 Gansu China 2020-01-22 12:00:00 0.0 0.0 0.0

Extracting Date and Time from the 'Last Update' column

In [20]:
data["Date"] = pd.to_datetime(data["Date"])
In [21]:
data['Last Update'] = pd.to_datetime(data['Last Update'])
data['Last Update'].head()
Out[21]:
0   2020-01-22 12:00:00
1   2020-01-22 12:00:00
2   2020-01-22 12:00:00
3   2020-01-22 12:00:00
4   2020-01-22 12:00:00
Name: Last Update, dtype: datetime64[ns]

Checking Date-wise Trend of confirmed patients and number of death

In [129]:
data.groupby('Date').sum()['Confirmed'].plot(color= 'y',linestyle='--', linewidth=3)
plt.grid()
In [128]:
data.groupby('Date').sum()['Deaths'].plot(color= 'r',linestyle='--', linewidth=3)
plt.grid()
In [124]:
data.groupby('Date').sum()['Recovered'].plot(color= 'g',linestyle='--', linewidth=3)
plt.grid()
In [125]:
data.groupby('Date').sum()[['Recovered','Deaths','Confirmed']].plot(linestyle='--', linewidth=3)
plt.grid()
In [139]:
data.groupby('Country').sum()[['Recovered','Deaths','Confirmed']].plot(linestyle='--', linewidth=3)
plt.grid()

Country-wise Distribution

In [113]:
country_count = data['Country'].value_counts().reset_index()
country_count.head()
Out[113]:
index Country
0 Mainland China 801
1 US 188
2 Australia 84
3 Canada 59
4 China 34
In [114]:
country_count = country_count.rename(columns={"index": "Country", "Country": "Count"})
country_count.head()
Out[114]:
Country Count
0 Mainland China 801
1 US 188
2 Australia 84
3 Canada 59
4 China 34
In [117]:
##country_count.set_index(['Country'],inplace=True)
##country_count.set_index(['Count'],inplace=True)
In [116]:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.barplot(x='Count',y='Country',data=country_count)
Out[116]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a702bc5f8>
In [97]:
country_count = pd.DataFrame(data.groupby('Country')['Confirmed'].max()).reset_index()
country_count.head()
Out[97]:
Country Confirmed
0 Australia 5.0
1 Belgium 1.0
2 Brazil 0.0
3 Cambodia 1.0
4 Canada 5.0
In [98]:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.barplot(x='Confirmed',y='Country',data=country_count)
Out[98]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a6fb126a0>
In [99]:
country_count_wo_mc=country_count[country_count.Country != 'Mainland China']
country_count_wo_mc.head()
Out[99]:
Country Confirmed
0 Australia 5.0
1 Belgium 1.0
2 Brazil 0.0
3 Cambodia 1.0
4 Canada 5.0
In [87]:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.barplot(x='Confirmed',y='Country',data=country_count_wo_mc)
Out[87]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a6f278c18>
In [119]:
conda install -c plotly plotly

Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: D:\anaconda

  added / updated specs:
    - plotly


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    conda-4.8.2                |           py37_0         2.8 MB
    plotly-4.5.2               |             py_0         3.9 MB  plotly
    retrying-1.3.3             |           py37_2          16 KB
    ------------------------------------------------------------
                                           Total:         6.7 MB

The following NEW packages will be INSTALLED:

  plotly             plotly/noarch::plotly-4.5.2-py_0
  retrying           pkgs/main/win-64::retrying-1.3.3-py37_2

The following packages will be UPDATED:

  conda                                       4.7.10-py37_0 --> 4.8.2-py37_0



Downloading and Extracting Packages

retrying-1.3.3       | 16 KB     |            |   0% 
retrying-1.3.3       | 16 KB     | #########9 |  99% 
retrying-1.3.3       | 16 KB     | ########## | 100% 

conda-4.8.2          | 2.8 MB    |            |   0% 
conda-4.8.2          | 2.8 MB    | #7         |  17% 
conda-4.8.2          | 2.8 MB    | ####4      |  45% 
conda-4.8.2          | 2.8 MB    | #######2   |  72% 
conda-4.8.2          | 2.8 MB    | ########## | 100% 

plotly-4.5.2         | 3.9 MB    |            |   0% 
plotly-4.5.2         | 3.9 MB    |            |   0% 
plotly-4.5.2         | 3.9 MB    |            |   1% 
plotly-4.5.2         | 3.9 MB    | 1          |   2% 
plotly-4.5.2         | 3.9 MB    | 3          |   3% 
plotly-4.5.2         | 3.9 MB    | 4          |   4% 
plotly-4.5.2         | 3.9 MB    | 6          |   6% 
plotly-4.5.2         | 3.9 MB    | 9          |  10% 
plotly-4.5.2         | 3.9 MB    | #3         |  14% 
plotly-4.5.2         | 3.9 MB    | #6         |  16% 
plotly-4.5.2         | 3.9 MB    | ##7        |  27% 
plotly-4.5.2         | 3.9 MB    | ###2       |  32% 
plotly-4.5.2         | 3.9 MB    | ####7      |  48% 
plotly-4.5.2         | 3.9 MB    | #####3     |  54% 
plotly-4.5.2         | 3.9 MB    | #######3   |  73% 
plotly-4.5.2         | 3.9 MB    | ########1  |  82% 
plotly-4.5.2         | 3.9 MB    | #########8 |  99% 
plotly-4.5.2         | 3.9 MB    | ########## | 100% 
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done

Note: you may need to restart the kernel to use updated packages.
In [130]:
import plotly.graph_objects as go
fig = go.Figure(data = [go.Pie(labels = country_count_wo_mc.Country,values = country_count_wo_mc.Confirmed)])
fig.update_layout(title="Country-wise distribution except Mainland China")
fig.show()
In [131]:
state_count = pd.DataFrame(data[data['Country']=='Mainland China'].groupby('Province/State').max()['Confirmed']).reset_index()
fig = go.Figure(data = [go.Pie(labels = state_count['Province/State'],values = state_count.Confirmed)])
fig.update_layout(title="State-wise distribution of Mainland China")
fig.show()

Growth trend of Corona virus in each Country individually except Mainland China

In [132]:
growth_country = pd.pivot_table(data[data.Country!='Mainland China'], values='Confirmed', columns='Country', index='Date')
growth_country.plot(figsize=(18,12))
Out[132]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a7148e400>

Growth trend of Corona virus in each Province/State of Mainland China individually

In [133]:
growth_state = pd.pivot_table(data[data['Country']=='Mainland China'], values='Confirmed', columns='Province/State', index='Date')
growth_state.plot(figsize=(18,12))
Out[133]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a76096320>

Country affected on the first day and number of confirmed cases on that date in each country.

In [142]:
initial_country=data[data['Date'] == data['Date'][0]]['Country'].unique()
initial_country
Out[142]:
array(['China', 'US', 'Japan', 'Thailand', 'South Korea'], dtype=object)
In [143]:
data[data['Date'] == data['Date'][0]].groupby('Country').sum()['Confirmed']
Out[143]:
Country
China          549.0
Japan            2.0
South Korea      1.0
Thailand         2.0
US               1.0
Name: Confirmed, dtype: float64

Total Confirmed, Recovery and Death in each country

In [150]:
global_case_wo_mc = data[data.Country != 'Mainland China'].groupby('Country')['Confirmed','Deaths','Recovered'].max().reset_index()
global_case_wo_mc
Out[150]:
Country Confirmed Deaths Recovered
0 Australia 5.0 0.0 4.0
1 Belgium 1.0 0.0 1.0
2 Brazil 0.0 0.0 0.0
3 Cambodia 1.0 0.0 1.0
4 Canada 5.0 0.0 1.0
5 China 444.0 0.0 0.0
6 Egypt 1.0 0.0 0.0
7 Finland 1.0 0.0 1.0
8 France 12.0 1.0 4.0
9 Germany 16.0 0.0 1.0
10 Hong Kong 60.0 1.0 2.0
11 India 3.0 0.0 3.0
12 Italy 3.0 0.0 0.0
13 Ivory Coast 0.0 0.0 0.0
14 Japan 66.0 1.0 12.0
15 Macau 10.0 0.0 5.0
16 Malaysia 22.0 0.0 7.0
17 Mexico 0.0 0.0 0.0
18 Nepal 1.0 0.0 1.0
19 Philippines 3.0 1.0 1.0
20 Russia 2.0 0.0 2.0
21 Singapore 77.0 0.0 24.0
22 South Korea 30.0 0.0 10.0
23 Spain 2.0 0.0 2.0
24 Sri Lanka 1.0 0.0 1.0
25 Sweden 1.0 0.0 0.0
26 Taiwan 22.0 1.0 2.0
27 Thailand 35.0 0.0 15.0
28 UK 9.0 0.0 8.0
29 US 3.0 0.0 2.0
30 United Arab Emirates 9.0 0.0 4.0
31 Vietnam 16.0 0.0 7.0
In [151]:
fig = go.Figure(data=[
    go.Bar(name='Confirmed', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Confirmed']),
    go.Bar(name='Deaths', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Deaths']),
    go.Bar(name='Recovered', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Recovered']),
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()
In [152]:
fig = go.Figure(data=[
    go.Bar(name='Confirmed', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Confirmed']),
    go.Bar(name='Deaths', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Deaths']),
    go.Bar(name='Recovered', x=global_case_wo_mc['Country'], y=global_case_wo_mc['Recovered']),
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()

Total Confirmed, Recovery and Death in each Province/State of Mainland China

In [153]:
mainland_case = data[data['Country']=='Mainland China'].groupby('Province/State')['Confirmed','Deaths','Recovered'].sum().reset_index()
mainland_case.head()
Out[153]:
Province/State Confirmed Deaths Recovered
0 Anhui 13621.0 46.0 1729.0
1 Beijing 5939.0 42.0 876.0
2 Chongqing 8702.0 50.0 1340.0
3 Fujian 4613.0 1.0 614.0
4 Gansu 1424.0 19.0 374.0
In [155]:
sns.barplot(x='Confirmed',y='Province/State',data=mainland_case)
Out[155]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a772b6a58>
In [154]:
fig = go.Figure(data=[
    go.Bar(name='Confirmed', x=mainland_case['Province/State'], y=mainland_case['Confirmed']),
    go.Bar(name='Deaths', x=mainland_case['Province/State'], y=mainland_case['Deaths']),
    go.Bar(name='Recovered', x=mainland_case['Province/State'], y=mainland_case['Recovered']),
])
# Change the bar mode
fig.update_layout(barmode='group')
fig.show()

Death Trend Globally

In [158]:
recovery_country = pd.pivot_table(data, values='Recovered', columns='Country', index='Date')
recovery_country.plot(figsize=(18,12))
Out[158]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a77fe7c88>
In [159]:
death_country = pd.pivot_table(data, values='Deaths', columns='Country', index='Date')
death_country.plot(figsize=(18,12))
Out[159]:
<matplotlib.axes._subplots.AxesSubplot at 0x20a7866bbe0>

Confirmed, Recovery and Death globally on world map

In [160]:
global_case = data.groupby('Country')['Confirmed','Deaths','Recovered'].max().reset_index()
global_case
Out[160]:
Country Confirmed Deaths Recovered
0 Australia 5.0 0.0 4.0
1 Belgium 1.0 0.0 1.0
2 Brazil 0.0 0.0 0.0
3 Cambodia 1.0 0.0 1.0
4 Canada 5.0 0.0 1.0
5 China 444.0 0.0 0.0
6 Egypt 1.0 0.0 0.0
7 Finland 1.0 0.0 1.0
8 France 12.0 1.0 4.0
9 Germany 16.0 0.0 1.0
10 Hong Kong 60.0 1.0 2.0
11 India 3.0 0.0 3.0
12 Italy 3.0 0.0 0.0
13 Ivory Coast 0.0 0.0 0.0
14 Japan 66.0 1.0 12.0
15 Macau 10.0 0.0 5.0
16 Mainland China 59989.0 1789.0 7862.0
17 Malaysia 22.0 0.0 7.0
18 Mexico 0.0 0.0 0.0
19 Nepal 1.0 0.0 1.0
20 Philippines 3.0 1.0 1.0
21 Russia 2.0 0.0 2.0
22 Singapore 77.0 0.0 24.0
23 South Korea 30.0 0.0 10.0
24 Spain 2.0 0.0 2.0
25 Sri Lanka 1.0 0.0 1.0
26 Sweden 1.0 0.0 0.0
27 Taiwan 22.0 1.0 2.0
28 Thailand 35.0 0.0 15.0
29 UK 9.0 0.0 8.0
30 US 3.0 0.0 2.0
31 United Arab Emirates 9.0 0.0 4.0
32 Vietnam 16.0 0.0 7.0

Importing the world_coordinates dataset

In [176]:
world_coordinates = pd.read_csv("world_coordinates.csv")
In [180]:
# Creating a dataframe with total no of confirmed cases for every country
Number_of_countries = len(data['Country'].value_counts())


cases = pd.DataFrame(data.groupby('Country')['Confirmed'].sum())
cases['Country'] = cases.index
cases.index=np.arange(1,Number_of_countries+1)

global_cases = cases[['Country','Confirmed']]
#global_cases.sort_values(by=['Confirmed'],ascending=False)
global_cases
Out[180]:
Country Confirmed
1 Australia 284.0
2 Belgium 14.0
3 Brazil 0.0
4 Cambodia 22.0
5 Canada 116.0
6 China 549.0
7 Egypt 4.0
8 Finland 20.0
9 France 186.0
10 Germany 252.0
11 Hong Kong 681.0
12 India 50.0
13 Italy 49.0
14 Ivory Coast 0.0
15 Japan 591.0
16 Macau 209.0
17 Mainland China 772951.0
18 Malaysia 303.0
19 Mexico 0.0
20 Nepal 24.0
21 Philippines 47.0
22 Russia 36.0
23 Singapore 797.0
24 South Korea 444.0
25 Spain 27.0
26 Sri Lanka 22.0
27 Sweden 18.0
28 Taiwan 320.0
29 Thailand 581.0
30 UK 93.0
31 US 250.0
32 United Arab Emirates 126.0
33 Vietnam 225.0
In [181]:
# Merging the coordinates dataframe with original dataframe
world_data = pd.merge(world_coordinates,global_cases,on='Country')
world_data.head()
Out[181]:
Code Country latitude longitude Confirmed
0 AE United Arab Emirates 23.424076 53.847818 126.0
1 AU Australia -25.274398 133.775136 284.0
2 BE Belgium 50.503887 4.469936 14.0
3 BR Brazil -14.235004 -51.925280 0.0
4 CA Canada 56.130366 -106.346771 116.0
In [182]:
# create map and display it
world_map = folium.Map(location=[10, -20], zoom_start=2.3,tiles='Stamen Toner')

for lat, lon, value, name in zip(world_data['latitude'], world_data['longitude'], world_data['Confirmed'], world_data['Country']):
    folium.CircleMarker([lat, lon],
                        radius=10,
                        popup = ('<strong>Country</strong>: ' + str(name).capitalize() + '<br>'
                                '<strong>Confirmed Cases</strong>: ' + str(value) + '<br>'),
                        color='red',
                        
                        fill_color='red',
                        fill_opacity=0.7 ).add_to(world_map)
world_map
Out[182]:
In [203]:
#Mainland China
China1 = data[data['Country']=='China']
China2 = data[data['Country']=='Mainland China']
China1.append(China2)
Out[203]:
Sno Date Province/State Country Last Update Confirmed Deaths Recovered
0 1 2020-01-22 12:00:00 Anhui China 2020-01-22 12:00:00 1.0 0.0 0.0
1 2 2020-01-22 12:00:00 Beijing China 2020-01-22 12:00:00 14.0 0.0 0.0
2 3 2020-01-22 12:00:00 Chongqing China 2020-01-22 12:00:00 6.0 0.0 0.0
3 4 2020-01-22 12:00:00 Fujian China 2020-01-22 12:00:00 1.0 0.0 0.0
4 5 2020-01-22 12:00:00 Gansu China 2020-01-22 12:00:00 0.0 0.0 0.0
5 6 2020-01-22 12:00:00 Guangdong China 2020-01-22 12:00:00 26.0 0.0 0.0
6 7 2020-01-22 12:00:00 Guangxi China 2020-01-22 12:00:00 2.0 0.0 0.0
7 8 2020-01-22 12:00:00 Guizhou China 2020-01-22 12:00:00 1.0 0.0 0.0
8 9 2020-01-22 12:00:00 Hainan China 2020-01-22 12:00:00 4.0 0.0 0.0
9 10 2020-01-22 12:00:00 Hebei China 2020-01-22 12:00:00 1.0 0.0 0.0
10 11 2020-01-22 12:00:00 Heilongjiang China 2020-01-22 12:00:00 0.0 0.0 0.0
11 12 2020-01-22 12:00:00 Henan China 2020-01-22 12:00:00 5.0 0.0 0.0
12 13 2020-01-22 12:00:00 Hong Kong China 2020-01-22 12:00:00 0.0 0.0 0.0
13 14 2020-01-22 12:00:00 Hubei China 2020-01-22 12:00:00 444.0 0.0 0.0
14 15 2020-01-22 12:00:00 Hunan China 2020-01-22 12:00:00 4.0 0.0 0.0
15 16 2020-01-22 12:00:00 Inner Mongolia China 2020-01-22 12:00:00 0.0 0.0 0.0
16 17 2020-01-22 12:00:00 Jiangsu China 2020-01-22 12:00:00 1.0 0.0 0.0
17 18 2020-01-22 12:00:00 Jiangxi China 2020-01-22 12:00:00 2.0 0.0 0.0
18 19 2020-01-22 12:00:00 Jilin China 2020-01-22 12:00:00 0.0 0.0 0.0
19 20 2020-01-22 12:00:00 Liaoning China 2020-01-22 12:00:00 2.0 0.0 0.0
20 21 2020-01-22 12:00:00 Macau China 2020-01-22 12:00:00 1.0 0.0 0.0
21 22 2020-01-22 12:00:00 Ningxia China 2020-01-22 12:00:00 1.0 0.0 0.0
22 23 2020-01-22 12:00:00 Qinghai China 2020-01-22 12:00:00 0.0 0.0 0.0
23 24 2020-01-22 12:00:00 Shaanxi China 2020-01-22 12:00:00 0.0 0.0 0.0
24 25 2020-01-22 12:00:00 Shandong China 2020-01-22 12:00:00 2.0 0.0 0.0
25 26 2020-01-22 12:00:00 Shanghai China 2020-01-22 12:00:00 9.0 0.0 0.0
26 27 2020-01-22 12:00:00 Shanxi China 2020-01-22 12:00:00 1.0 0.0 0.0
27 28 2020-01-22 12:00:00 Sichuan China 2020-01-22 12:00:00 5.0 0.0 0.0
28 29 2020-01-22 12:00:00 Taiwan China 2020-01-22 12:00:00 1.0 0.0 0.0
29 30 2020-01-22 12:00:00 Tianjin China 2020-01-22 12:00:00 4.0 0.0 0.0
... ... ... ... ... ... ... ... ...
1645 1646 2020-02-17 22:00:00 Guangdong Mainland China 2020-02-17 12:43:02 1322.0 4.0 524.0
1646 1647 2020-02-17 22:00:00 Henan Mainland China 2020-02-17 14:53:02 1246.0 16.0 509.0
1647 1648 2020-02-17 22:00:00 Zhejiang Mainland China 2020-02-17 11:23:06 1171.0 0.0 507.0
1648 1649 2020-02-17 22:00:00 Hunan Mainland China 2020-02-17 11:43:01 1006.0 3.0 498.0
1649 1650 2020-02-17 22:00:00 Anhui Mainland China 2020-02-17 09:13:07 973.0 6.0 280.0
1650 1651 2020-02-17 22:00:00 Jiangxi Mainland China 2020-02-17 01:33:02 930.0 1.0 275.0
1651 1652 2020-02-17 22:00:00 Jiangsu Mainland China 2020-02-17 10:23:04 626.0 0.0 258.0
1652 1653 2020-02-17 22:00:00 Chongqing Mainland China 2020-02-17 23:13:06 553.0 5.0 225.0
1653 1654 2020-02-17 22:00:00 Shandong Mainland China 2020-02-17 11:03:06 541.0 2.0 191.0
1654 1655 2020-02-17 22:00:00 Sichuan Mainland China 2020-02-17 13:13:09 495.0 3.0 156.0
1655 1656 2020-02-17 22:00:00 Heilongjiang Mainland China 2020-02-17 08:03:06 457.0 11.0 85.0
1657 1658 2020-02-17 22:00:00 Beijing Mainland China 2020-02-17 01:23:02 381.0 4.0 114.0
1658 1659 2020-02-17 22:00:00 Shanghai Mainland China 2020-02-17 23:53:02 333.0 1.0 161.0
1659 1660 2020-02-17 22:00:00 Hebei Mainland China 2020-02-17 13:03:06 301.0 3.0 122.0
1660 1661 2020-02-17 22:00:00 Fujian Mainland China 2020-02-17 10:23:04 290.0 0.0 90.0
1661 1662 2020-02-17 22:00:00 Shaanxi Mainland China 2020-02-17 11:43:01 240.0 0.0 79.0
1662 1663 2020-02-17 22:00:00 Guangxi Mainland China 2020-02-17 10:23:04 238.0 2.0 53.0
1663 1664 2020-02-17 22:00:00 Yunnan Mainland China 2020-02-17 10:23:04 171.0 0.0 47.0
1664 1665 2020-02-17 22:00:00 Hainan Mainland China 2020-02-17 11:03:06 163.0 4.0 59.0
1665 1666 2020-02-17 22:00:00 Guizhou Mainland China 2020-02-17 11:13:16 146.0 1.0 57.0
1666 1667 2020-02-17 22:00:00 Shanxi Mainland China 2020-02-17 22:53:01 130.0 0.0 53.0
1667 1668 2020-02-17 22:00:00 Tianjin Mainland China 2020-02-17 12:43:02 125.0 3.0 46.0
1668 1669 2020-02-17 22:00:00 Liaoning Mainland China 2020-02-17 11:13:16 121.0 1.0 43.0
1669 1670 2020-02-17 22:00:00 Gansu Mainland China 2020-02-17 14:53:02 91.0 2.0 58.0
1670 1671 2020-02-17 22:00:00 Jilin Mainland China 2020-02-17 11:03:06 89.0 1.0 34.0
1672 1673 2020-02-17 22:00:00 Xinjiang Mainland China 2020-02-17 01:53:01 75.0 1.0 12.0
1673 1674 2020-02-17 22:00:00 Inner Mongolia Mainland China 2020-02-17 02:03:06 72.0 0.0 8.0
1674 1675 2020-02-17 22:00:00 Ningxia Mainland China 2020-02-17 11:03:06 70.0 0.0 35.0
1681 1682 2020-02-17 22:00:00 Qinghai Mainland China 2020-02-15 08:43:03 18.0 0.0 13.0
1708 1709 2020-02-17 22:00:00 Tibet Mainland China 2020-02-12 06:43:02 1.0 0.0 1.0

835 rows × 8 columns

In [185]:
import pycountry
countries = {}
for country in pycountry.countries:
    countries[country.name] = country.alpha_3
country_count["iso_alpha"] = country_count['Country'].map(countries.get)

There are some countries whose iso_alpha value is not present and it is returning it as None. Droping those records.

In [186]:
filetered_country=country_count.dropna()
filetered_country.head()
Out[186]:
Country Count iso_alpha
2 Australia 84 AUS
3 Canada 59 CAN
4 China 34 CHN
5 Thailand 27 THA
7 Japan 27 JPN
In [189]:
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.choropleth(filetered_country, locations="iso_alpha",
                    color='Count',
                    color_continuous_scale=px.colors.sequential.Plasma,
                    projection="natural earth")
fig.show()
In [ ]: